code: def _replace_module(
      model,
      policies,
      prefix="",
      layer_id=0,
      level_id=0,
      state_dict=None,
  ):
      """Traverse model's children recursively and apply any transformations in ``policies``.
      Arguments:
          model (torch.nn.Module): model to augment
          policies (dict): Mapping of source class to replacement function.
      Returns:
          Modified ``model``.
      """
      for name, child in model.named_children():
          if child.__class__ in policies:
              replaced_module = policies[child.__class__][0](
                  child,
                  policies[child.__class__][-1],
                  layer_id,
                  prefix=prefix + name,
                  state_dict=state_dict,
              )
              setattr(model, name, replaced_module)
              if isinstance(model, PipelineModule):
                  assert hasattr(
                      model, "forward_funcs"
                  ), "we require pipe-module to have the list of fwd_functions"
                  model.forward_funcs[
                      model.fwd_map[name]
                  ] = replaced_module
              layer_id += 1
          else:
              checking_key = prefix + name + "."
              if (
                  Loading.is_load_module(child)
                  and state_dict is not None
              ):
                  if any(checking_key in item for item in state_dict):
                      Loading.load(
                          child,
                          state_dict,
                          checking_key,
                      )
                  else:
                      continue
              if len(child._buffers) != 0 and state_dict is not None:
                  Loading.load_buffer(child, state_dict, checking_key)
              _, layer_id = _replace_module(
                  child,
                  policies,
                  prefix
                  if level_id == 0
                  and skip_level_0_prefix(model, state_dict)
                  else prefix + name + ".",
                  layer_id=layer_id,
                  level_id=level_id + 1,
                  state_dict=state_dict,
              )
  
      # Add the reset_cache func to the model, so that it can be called in the beginning of text-generation.
      model.reset_cache = (
          transformer_inference.DeepSpeedTransformerInference.reset_cache
      )
      return model, layer_id
  
imports: import os
  import torch
  import tqdm
  import deepspeed
  import deepspeed.ops.transformer
  from deepspeed.ops.transformer.inference.diffusers_attention import DeepSpeedDiffusersAttention
  from deepspeed.ops.transformer.inference.diffusers_transformer_block import DeepSpeedDiffusersTransformerBlock
  from deepspeed.ops.transformer.inference.diffusers_2d_transformer import Diffusers2DTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from .replace_policy import replace_policies, generic_policies
  from .auto_tp import AutoTP, ReplaceWithTensorSlicing, Loading
  from deepspeed import comm
  from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd
  from .load_checkpoint import load_model_with_checkpoint
  import time
  from .utils import policy_to_ds_container
  import gc
  from ..pipe import PipelineModule
  import re
label_desc: Use image node_id 8 for calling a global function with example usage: _replace_module(model, policies, prefix, layer_id, level_id, state_dict) and returns: model,  layer_id